Investigating the Impact of International Markets and New Digital Technologies on Business Innovation in Emerging Markets

International markets and digital technologies are considered among the factors affecting business innovation. The emergence and deployment of digital technologies in emerging markets increase the innovation potential in businesses. Companies with an entrepreneurial orientation also strengthen their innovation capabilities. The present study aimed to investigate the impact of international markets and new digital technologies on business innovation in emerging markets, and to estimate the mediating effect of entrepreneurial orientation on this relationship. The present research was applied research in terms of aim and descriptive survey in terms of data collection method and quantitative in terms of the type of collected data. A standard questionnaire was to collect data. The study’s statistical population consisted of all companies providing business services in Tehran, Iran. To analyse the data, the structural equation modelling method with partial least squares method and Smart PLS-3 Software was used. The results revealed that international markets and digital technologies are positively associated with innovation. They also revealed that when a company’s entrepreneurial orientation increases, the digital technologies and international markets will be more involved in mutual relationships.

Does the Prevalence of Obesity Affect the Demand for Soft Drinks? Evidence from Cross-Country Panel Data

The impact of soft drinks on obesity has been widely investigated during the last decades. Conversely, the role of obesity as a factor influencing the demand for soft drinks remains largely unexplored. However, understanding potential changes in the demand for soft drinks, as a result of changes in the spread of obesity, may be useful to better design a comprehensive strategy to curb soft drink consumption. In this paper, we aim to answer the following research question: Does the prevalence of obesity affect the demand for soft drinks? For this purpose, we collected data in a sample of 97 countries worldwide for the period 2005–2019. To deal with problems of reverse causality, an instrumental variable approach and a two-stage least squares method were used to estimate the impact of the age-standardized obesity rate on the market demand for soft drinks. After controlling for several demographic and socio-economic confounding factors, we found that a one percent increase in the prevalence of obesity increases the consumption of soft drinks and carbonated soft drinks by about 2.37 and 1.11 L per person/year, respectively. Our findings corroborate the idea that the development of an obesogenic food environment is a self-sustaining process, in which obesity and unhealthy lifestyles reinforce each other, and further support the need for an integrated approach to curb soft drink consumption by combining sugar taxes with bans, regulations, and nutrition education programs.

Methods for Fitting the Limit State Function of the Residual Strength of Damaged Ships

The limit state function is important for the assessment of the longitudinal strength of damaged ships under combined bending moments in severe waves. As the limit state function cannot be obtained directly, the common approach is to calculate the results for the residual strength and approximate the limit state function by fitting, for which various methods have been proposed. In this study, four commonly used fitting methods are investigated: namely, the least-squares method, the moving least-squares method, the radial basis function neural network method, and the weighted piecewise fitting method. These fitting methods are adopted to fit the limit state functions of four typically sample distribution models as well as a damaged tanker and damaged bulk carrier. The residual strength of a damaged ship is obtained by an improved Smith method that accounts for the rotation of the neutral axis. Analysis of the results shows the accuracy of the linear least-squares method and nonlinear least-squares method, which are most commonly used by researchers, is relatively poor, while the weighted piecewise fitting method is the better choice for all investigated combined-bending conditions.

Evaluation of the Strength Characteristics of Silty-Clayey Soils during Freezing-Thawing Cycles

Destructuring settlements due to frost heave during the structures’ exploitation are often not taken into account at the designing stage, although they are indirectly related to the bearing capacity of the soils. The objective of this research was analyzing the effect of the number of freezing-thawing cycles on the strength characteristics of soils. A paired experiment with various initial parameters (void ratio, initial moisture content, and the number of freezing-thawing cycles) was carried out. According to the experimental results, the cohesion largely depends on the above parameters which might lead to its decrease by up to three times. The angle of internal friction demonstrated an indefinite behavior during the freeze-thaw cycles, which is confirmed by a literature review. Freezing–thawing cycles significantly decrease the soil bearing capacity: up to 44% after 10 freezing-thawing cycles for soil with and . However, in the case of and it increased by 33%. A program based on the least-squares method was used to calculate the approximation coefficients of the dependence describing the changes in strength characteristics from the abovementioned parameters. Changes in strength characteristics must be taken into account when designing structures, as they can lead to additional settlement or even subsidence of the foundations.

Uncertainty Analysis and Experimental Validation of Identifying the Governing Equation of an Oscillator Using Sparse Regression

In recent years, the rapid growth of computing technology has enabled identifying mathematical models for vibration systems using measurement data instead of domain knowledge. Within this category, the method Sparse Identification of Nonlinear Dynamical Systems (SINDy) shows potential for interpretable identification. Therefore, in this work, a procedure of system identification based on the SINDy framework is developed and validated on a single-mass oscillator. To estimate the parameters in the SINDy model, two sparse regression methods are discussed. Compared with the Least Squares method with Sequential Threshold (LSST), which is the original estimation method from SINDy, the Least Squares method Post-LASSO (LSPL) shows better performance in numerical Monte Carlo Simulations (MCSs) of a single-mass oscillator in terms of sparseness, convergence, identified eigenfrequency, and coefficient of determination. Furthermore, the developed method SINDy-LSPL was successfully implemented with real measurement data of a single-mass oscillator with known theoretical parameters. The identified parameters using a sweep signal as excitation are more consistent and accurate than those identified using impulse excitation. In both cases, there exists a dependency of the identified parameter on the excitation amplitude that should be investigated in further research.

Does corporate social responsibility affect dividend policy? Empirical evidence in the French context

Purpose The purpose of this paper is to focus on the impact of corporate social responsibility (CSR) on dividend policy in the French context. In addition, the authors seek to determine if the individual components of CSR influence dividend policy. Design/methodology/approach This study uses panel data methodology for a sample of French non-financial firms between 2008 and 2018. Generalized least squares method is used to estimate the models. Findings Using panel data methodology for a sample of 825 observations for the period 2008–2018, this study finds a positive impact of CSR practices on dividend policy. The authors also find that individual components of CSR positively influence dividend policy. To check the robustness of the results, this study further runs a sensitivity tests, including an alternative measure of dividend policy, all of which confirm the findings. Practical implications This study has examined the impact of CSR on dividend policy in France and may have implications for regulatory, investors, analysts and academics. First, the involvement in CSR best practices encourages companies to pay more dividends to investors. Therefore, investors are more motivated to invest in socially responsible firms than socially irresponsible firms. Second, given the association of CSR with the quality of accounting information and financial markets, regulators should step up recommendations relating to the different societal dimensions of CSR. Originality/value While little previous work has focused on the causal link between CSR and dividend policy, this research is the first, to the authors’ knowledge, to have looked at the impact of CSR on dividend policy in France.

A Regularised Total Least Squares Approach for 1D Inverse Scattering

We study the inverse scattering problem for a Schrödinger operator related to a static wave operator with variable velocity, using the GLM (Gelfand–Levitan–Marchenko) integral equation. We assume to have noisy scattering data, and we derive a stability estimate for the error of the solution of the GLM integral equation by showing the invertibility of the GLM operator between suitable function spaces. To regularise the problem, we formulate a variational total least squares problem, and we show that, under certain regularity assumptions, the optimisation problem admits minimisers. Finally, we compute numerically the regularised solution of the GLM equation using the total least squares method in a discrete sense.

Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

PROCESSING SUNSHINE DURATION MEASUREMENTS FOR THE ASSESSMENT OF SOLAR RADIATION IN CLIMATIC REGIONS OF THE CENTRAL AFRICAN REPUBLIC

The objectives of this work carried out in the Central African Republic are to propose new correlations between the components of solar radiation and the sunshine duration on a horizontal surface on the ground, and then to make an evaluation of the solar potential in the cities of Bambari, Birao and Bangui. Polynomial regression models were used and their parameters were estimated by the ordinary least squares method. A statistical evaluation allowed us to compare the performance of the models. The best correlations are then used to estimate the global and diffuse radiation. In the city of Birao, the estimated global radiation is around 6 kWh/m2.j and the diffuse radiation around 2 kWh/m2.j ; in Bambari the global radiation is around 5.4 kWh/m2.j and the diffuse around 2.3 kWh/m2.j ; in Bangui the global radiation is around 5 kWh/m2.j and the diffuse radiation around 2.3 kWh/m2.j. The potential solar in all these regions is very favorable for small and large-scale solar photovoltaic applications.